Estimating questions? difficulty levels is an important task in community question answering (CQA) services. Previous stud- ies propose to solve this problem based on the question-user comparisons extract- ed from the question answering threads. However, they suffer from data sparseness problem as each question only gets a lim- ited number of comparisons. Moreover, they cannot handle newly posted question- s which get no comparisons. In this pa- per, we propose a novel question difficul- ty estimation approach called Regularized Competition Model (RCM), which natu- rally combines question-user comparisons and questions? textual descriptions into a unified framework. By incorporating tex- tual information, RCM can effectively deal with data sparseness problem. We further employ a K-Nearest ...